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[국내논문] Automated Markerless Analysis of Human Gait Motion for Recognition and Classification 원문보기

ETRI journal, v.33 no.2, 2011년, pp.259 - 266  

Yoo, Jang-Hee (Software Research Laboratory, ETRI) ,  Nixon, Mark S. (School of Electronics and Computer Science, University of Southampton)

Abstract ▼ AI-Helper 아이콘AI-Helper

We present a new method for an automated markerless system to describe, analyze, and classify human gait motion. The automated system consists of three stages: I) detection and extraction of the moving human body and its contour from image sequences, ii) extraction of gait figures by the joint angle...

Keyword

#Human motion   #gait analysis   #biometrics  

AI 본문요약
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제안 방법

  • In this paper, we propose a new approach to an automated markerless system for describing, analyzing, and classifying human gait by computer vision techniques without subject contact or intervention. The human body and its contour are extracted from the image sequences from one of the largest video gait databases, which comprises digital video (DV) recordings of walking subjects.
  • The trajectories of the joint angles followed the earlier results of medical studies. Also, the gait features based on the motion parameters were extracted, and the k-NN classifier was used to analyze the discriminatory ability of the extracted features. The results produced classification rates of 97% CCR for 30 subjects and 84% CCR for 100 subjects.

대상 데이터

  • An image sequence contains only a single subject walking at normal speed and was acquired at 25 fps with 720×576 color pixels from good quality progressive scan DV cameras.
  • In the experiments, 100 different subjects (16 females and 84 males) with seven image sequences of each subject from the SOTON indoor track database, a total of 700 image sequences (≅19,534 images), are used.
  • Undoubtedly, a more sophisticated classifier would be prudent, but the interest here is to examine the genuine discriminatory ability of the features. A total of the 500 feature vectors extracted from the front four of the seven sequences and their means for each of 100 different subjects are used as the training samples. Also, a total of 100 feature vectors extracted from the means of the remaining three of the seven sequences for each of 100 different subjects are used as the test samples.
  • A total of the 500 feature vectors extracted from the front four of the seven sequences and their means for each of 100 different subjects are used as the training samples. Also, a total of 100 feature vectors extracted from the means of the remaining three of the seven sequences for each of 100 different subjects are used as the test samples.

이론/모형

  • To classify the gait patterns, a simple k-NN algorithm is employed as a classifier applied to a feature space comprised of 10 features based on the motion parameters: body height, cycle time, stride length, speed, average joint angles, variation of hip angles, the correlation coefficient between the left and right leg angles, and the center coordinates of the hip-knee cyclogram.
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참고문헌 (22)

  1. V.T. Inman, H.J. Ralston, and F. Todd, Human Walking, Baltimore: Williams & Wilkins, 1981. 

  2. D.A. Winter, The Biomechanics and Motor Control of Human Gait: Normal, Elderly and Pathological, Ontario: Waterloo Biomechanics, 1991. 

  3. M.S. Nixon, T. Tan, and R. Chellappa, Human Identification Based on Gait, Springer, 2006. 

  4. M.K. Leung and Y.H. Yang, "First Sight: A Human Body Outline Labeling System," IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 4, 1995, pp.359-377. 

    상세보기

  5. J. Perry, Gait Analysis: Normal and Pathological Function, NJ: Slack, 1992. 

  6. G. Johansson, "Visual Perception of Biological Motion and a Model for Its Analysis," Perception and Psychophysics, vol. 14, no. 2, 1973, pp. 201-211. 

    상세보기

  7. S.V. Stevenage, M.S. Nixon, and K. Vince, "Visual Analysis of Gait as a Cue to Identity," Applied Cognitive Psychology, vol. 13, 1999, pp. 513-526. 

    상세보기

  8. M.P. Murray, A.B. Drought, and R.C. Kory, "Walking Patterns of Normal Men," J. Bone and Joint Surgery, vol. 46A, no. 2, 1964, pp. 335-360. 

  9. C.W. Cho et al., "A Vision-Based Analysis System for Gait Recognition in Patients with Parkinson's Disease," Expert Syst. with Appl., vol. 36, no. 3, Apr. 2009, pp. 7033-7039. 

  10. J.D. Shutler et al., "On a Large Sequence-Based Human Gait Database," Proc. Recent Advances in Soft Computing, Nottingham, UK, 2002. 

  11. R. Poppe, "Vision-Based Human Motion Analysis: An Overview," Comput. Vision and Image Understanding, vol. 108, nos. 1-2, 2007, pp. 4-18. 

  12. T. Ding, "A Robust Identification Approach to Gait Recognition," Proc. IEEE Conf. Comput. Vision Pattern Recog., Alaska, USA, 2008. 

  13. R.D. Seely et al., "View Invariant Gait Recognition," Handbook of Remote Biometrics, Springer, 2009, pp. 61-81. 

  14. A. Tsuji, Y. Makihara, and Y. Yagi, "Silhouette Transformation Based on Walking Speed for Gait Identification," Proc. IEEE Conf. Comput. Vision Pattern Recog., San Francisco, USA, 2010. 

  15. K. Bashir, T. Xiang, and S. Gong, "Gait Recognition without Subject Cooperation," Pattern Recog. Lett., 2010. 

  16. M. Gleicher, "Animation from Observation: Motion Capture and Motion Editing," Comput. Graphics, vol. 33, no. 4, 1999, pp. 51-55. 

  17. M.W. Whittle, Gait Analysis: An Introduction, Oxford: Butterworth Heinemann, 2002. 

  18. R.F.M. Kleissen et al., "Electromyography in the Biomechanical Analysis of Human Movement and its Clinical Application," Gait and Posture, vol. 8, no. 2, 1988, pp.143-158. 

  19. T.B. Moeslund and E. Granum, "A Survey of Computer Vision-Based Human Motion Capture," Comput. Vision and Image Understanding, vol. 81, no. 3, 2001, pp.231-268. 

  20. J.K. Aggarwal and Q. Cai, "Human Motion Analysis: A Review," Comput. Vision and Image Understanding, vol. 73, no. 3, 1999, pp.428-440. 

  21. W.T. Dempster and G.R.L. Gaughran, "Properties of Body Segments Based on Size and Weight," American J. Anatomy, vol. 120, 1967, pp. 33-54. 

  22. R. Drillis and R. Contini, "Body Segment Parameters," Technical Report 1163-03, New York University, New York, 1966. 

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